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This study aims to investigate organizational learning, represented by the absorptive capacity, as a condition for the firm to learn about marketing data and make more informed decisions. The authors also aimed to understand how the behavior of micro, small and medium enterprises (MSME) businesses differ in this scenario through a multilevel perspective.

Placing absorptive capacity as a mediator of the relationship between business analytics and rational marketing decisions, the authors analyzed data from 224 Brazilian retail companies using structural equation modeling estimated with partial least squares. To test the cross-level moderation effect, the authors also performed a multilevel analysis in RStudio.

The authors found a partial mediation of the absorptive capacity in the relation between business analytics and rational marketing decisions. The authors also discovered that, in the MSMEs firms’ group, even if smaller companies find it more difficult to use data, those that do may reap more benefits than larger ones. This is due to the influence of size in how firms handle information.

The sample size, despite having shown to be consistent and valid, is considered small for a multilevel study. This suggests that our multilevel results should be viewed as suggestive, rather than conclusive, and subjected to further validation.

Rather than solely positioning business analytics as a tool for decision support, the authors’ analysis highlights the importance for firms to develop the absorptive capacity to enable ongoing acquisition, exploration and management of knowledge.

MSMEs are of economic and social importance to most countries, especially developing ones. This research aimed to improve understanding of how this group of firms could transform knowledge into better decisions. The authors also highlight micro and small firms’ difficulties with the use of marketing data so that they can have more effective practices.

The research contributes to the understanding of organizational mechanisms to absorb and learn from the vast amount of current marketing information. Recognizing the relevance of MSMEs, a preliminary multilevel analysis was also conducted to comprehend differences within this group.

Although businesses continue to take up artificial intelligence (AI), concerns remain that companies are not realising the full value of their investments. The study aims to provide insights into how AI creates business value by investigating the mediating role of Business Process Management (BPM) capabilities.

The integrative model of IT Business Value was contextualised, and structural equation modelling was applied to validate the proposed serial multiple mediation model using a sample of 448 organisations based in the EU.

The results validate the proposed serial multiple mediation model according to which AI adoption increases organisational performance through decision-making and business process performance. Process automation, organisational learning and process innovation are significant complementary partial mediators, thereby shedding light on how AI creates business value.

In pursuing a complex nomological framework, multiple perspectives on realising business value from AI investments were incorporated. Several moderators presenting complementary organisational resources (e.g. culture, digital maturity, BPM maturity) could be included to identify behaviour in more complex relationships. The ethical and moral issues surrounding AI and its use could also be examined.

The provided insights can help guide organisations towards the most promising AI activities of process automation with AI-enabled decision-making, organisational learning and process innovation to yield business value.

While previous research assumed a moderated relationship, this study extends the growing literature on AI business value by empirically investigating a comprehensive nomological network that links AI adoption to organisational performance in a BPM setting.

The COVID-19 pandemic has significantly affected service small- and medium-sized enterprises (SMEs), increasing the importance of understanding how these businesses can become more resilient and how service innovation can be an effective strategy to increase their adaptive capacity and survival. This study aims to examine the role of dynamic capabilities in service innovation as a factor explaining the resilience of SMEs in Puerto Rico and the Dominican Republic during the COVID-19 crisis and its impact on service innovation. Additionally, the authors assess whether service innovation has a significant impact on value cocreation in these businesses.

This study used a quantitative method by surveying 118 SME owners in Puerto Rico and the Dominican Republic. The data were analyzed using partial least-squares structural equation modeling.

The results reflect important theoretical contributions by analyzing resilience from an innovation perspective instead of a retrospective approach, which is an area that has not been analyzed in the literature. Additionally, theoretical contributions to marketing services in SMEs are discussed, which is an underresearched topic. The results advance by discussing the role of service innovation through the reconfiguration of resources and how this can be an effective strategy to increase value cocreation with customers during crises.

This study is original in that it analyzes resilience from the perspective of innovation, and not from a retrospective approach. It offers a vision in response to the need for studies that provide a clearer conceptualization of resilience in small businesses. This highlights the importance of considering regional differences and service innovation as effective strategies to enhance resilience and value cocreation with customers.

This study reviews scholarly work in sustainable green logistics and remanufacturing (SGLR) and their subdisciplines, in combination with bibliometric, thematic and content analyses that provide a viewpoint on categorization and a future research agenda. This paper provides insight into current research trends in the subjects of interest by examining the most essential and most referenced articles promoting sustainability and climate-neutral logistics.

For the literature review, the authors extracted and sifted 2180 research and review papers for the period 2008–2023 from the Scopus database. The authors performed bibliometric and content analyses using multiple software programs such as Gephi, VOSviewer and R programming.

The SGLR papers can be grouped into seven clusters: (1) The circular economy facets; (2) Decarbonization of operations to nurture a climate-neutral business; (3) Green sustainable supply chain management; (4) Drivers and barriers of reverse logistics and the circular economy; (5) Business models for sustainable logistics and the circular economy; (6) Transportation problems in sustainable green logistics and (7) Digitalization of logistics and supply chain management.

In this review, fundamental ideas are established, research gaps are identified and multiple future research subjects are proposed. These propositions are categorized into three main research streams, i.e. (1) Digitalization of SGLR, (2) Enhancing scopes, sectors and industries in the context of SGLR and (3) Developing more efficient and effective climate-neutral and climate change-related solutions and promoting more environmental-related and sustainability research concerning SGLR. In addition, two conceptual models concerning SGLR and climate-neutral strategies are developed and presented for managers and practitioners to consider when adopting green and sustainability principles in supply chains. This review also highlights the need for academics to go beyond frameworks and build new techniques and instruments for monitoring SGLR performance in the real world.

This study provides an overview of the evolution of SGLR; it also clarifies concepts, environmental concerns and climate change practices, particularly those directed to supply chain management.

Taking the dialogic organizational development perspective, this study aims to investigate the framing processes when engaging in dialogue for strategy implementation and how these enable the evolution of implementation opportunities.

Through a qualitative exploratory study conducted in a large multinational, the authors analyse the dialogue and interactions among 25 dyads when identifying opportunities to contribute to strategy implementation. The data analysis relies on a process-coding approach and linkography, a valuable protocol analysis for identifying recursive interaction schemas in conversations.

The authors identify four main framing processes – shaping, unveiling, scattering and shifting – and provide a framework of how these processes affect individuals’ mental models through increasing the tangibility of opportunities or elevating them to new value hierarchies.

From a theoretical perspective, this study contributes to the strategy implementation and organizational development literature, providing a micro-perspective of how dialogue allows early knowledge structures to emerge and shape the development of opportunities for strategy implementation.

From a managerial perspective, the authors offer insights to trigger action and change in individuals to contribute to strategy when moving from formulation to implementation.

Rather than focusing on the structural control view of strategy implementation and the role of the top management team, this study considers strategy implementation as a practice and what it takes for organizational actors who do not take part in strategy formulation to enact and shape opportunities for strategy implementation through constructive dialogue.

Supply chain (SC) management is more challenging than ever. Significantly, the pandemic has provoked global and economic destruction that appeared in the manufacturing industry as a “black swan.” Therefore, the purpose of this study was to examine the role of information processing and digital supply chain in supply chain resilience through supply chain risk management.

This study examines SC risk management and resilience from an information processing theory perspective. The authors used data collected from 251 SC professionals in the manufacturing industry, and the authors used a quantitative method to analyze the data. The data was analyzed using partial least squares-structural equation modeling. To confirm the higher-order measurement model, the authors used SmartPLS version 4 software.

This study found that information processing capability (disruptive orientation and visibility in high-order) and digital SC significantly and positively affect SC risk management and resilience. Similarly, SC risk management positively mediates the relationship between information processing capability and digital SC. However, information processing capability was found to have a more substantial effect on SC risk management than the digital SC.

This study has both academic and practical contributions. It contributed to existing information processing theory, and manufacturing firms can improve their performance by proactively responding to SC disruptions by recognizing the pivotal role of study variables in risk management for a resilient SC.

The conceptual model of this study is based on information processing theory, which asserts that synchronizing information processing capabilities and digital SCs allows a firm to deal with unplanned events. SC disruption orientation and visibility are considered risk controllers as they allow the firms to be more proactive. An integrated model of conceptualizing the disruption orientation, visibility (higher-order) and digital SC with information processing theory makes this research novel.

The purpose of this paper is to investigate how different manufacturing technologies are bundled together and how these bundles influence operations performance and, indirectly, business performance. With the emergence of Industry 4.0 (I4.0) technologies, manufacturing companies can use a wide variety of advanced manufacturing technologies (AMT) to build an efficient and effective production system. Nevertheless, the literature offers little guidance on how these technologies, including novel I4.0 technologies, should be combined in practice and how these combinations might have a different impact on performance.

Using a survey study of 165 manufacturing plants from 11 different countries, we use factor analysis to empirically derive three distinct manufacturing technology bundles and structural equation modeling to quantify their relationship with operations and business performance.

Our findings support an evolutionary rather than a revolutionary perspective. I4.0 technologies build on traditional manufacturing technologies and do not constitute a separate direction that would point towards a fundamental digital transformation of companies within our sample. Performance effects are rather weak: out of the three technology bundles identified, only “automation and robotization” have a positive influence on cost efficiency, while “base technologies” and “data-enabled technologies” do not offer a competitive advantage, neither in terms of cost nor in terms of differentiation. Furthermore, while the business performance impact is positive, it is quite weak, suggesting that financial returns on technology investments might require longer time periods.

Relying on a complementarity approach, our research offers a novel perspective on technology implementation in the I4.0 era by investigating novel and traditional manufacturing technologies together.

The growing awareness of climate risks associated with food safety issues has drawn the attention of stakeholders urging the food industry to carry out a sustainable food safety management system (FSMS). This study aims to investigate whether the critical success factors (CSFs) of sustainable FSMS can contribute to achieving climate neutrality, and how the adoption of FSMS 4.0 supported by the Industry Revolution 4.0 (IR 4.0) technologies moderates the impact of the CSFs on achieving climate neutrality.

Survey data from 255 food production firms in China and Vietnam were utilised for the empirical analysis. The research hypotheses were examined using structural equations modelling (SEM) with route analysis and bootstrapping techniques.

The results show that top management support, human resource management, infrastructure and integration appear as the significant CSFs that directly impact food production firms in achieving climate neutrality. Moreover, the results demonstrate that the adoption of FSMS 4.0 integrated with the three components (ecosystems, quality standards and robustness) significantly moderates the impact of the CSFs on achieving climate neutrality with lower inputs in human resources, infrastructure investment, integration and external assistance, and higher inputs in strengthening food safety administration.

This study provides empirical findings that fill the research gap in understanding the relationship between climate neutrality and the CSFs of sustainable FSMS while considering the moderating effects of the FSMS 4.0 components. The results provide theoretical and practical insights into how the food production sector can utilise IR 4.0 to attain sustainable FSMS for achieving climate neutrality.

The advancements in internet technologies and the use of sophisticated digital devices in supply chain operations incessantly generate enormous amounts of data, which is termed as big data (BD). The BD technologies have brought about a paradigm shift in the supply chain decision-making towards profitability and sustainability. The aim of this work is to address the issue of implementation of the big data analytics (BDA) in sustainable supply chain management (SSCM) by identifying the relevant factors and developing a structural model for this purpose.

Through a comprehensive literature review and experts’ opinion, the crucial factors are found using the PESTEL framework, which covers political, economic, social, technological, environmental and legal factors. The structural model is developed based on the results of the total interpretive structural modelling (TISM) procedure and MICMAC analysis.

The policy support regarding IT, culture of data-based decision-making, inappropriate selection of BDA technologies and the laws related to data security and privacy are found to affect most of the other factors. Also, the company’s vision towards environmental performance and willingness for material and energy optimization are found to be crucial for the environmental and social sustainability of the supply chain.

The study is focused on the manufacturing supply chain in emerging economies. It may be extended to other industry sectors and geographical areas. Also, additional factors may be included to make the model more robust.

The proposed model imparts an understanding of the relative importance and interrelationship of factors. This may be useful to managers to assess their strengths and weaknesses and ascertain their priorities in the context of their organization for developing a suitable investment plan.

The study establishes the importance of BDA for conservation and management of energy and material. This is crucial to develop strategies for enhancing eco-efficiency of the supply chain, which in turn enhances the economic returns for the society.

This study addresses the implementation of BDA in SSCM in the context of emerging economies. It uses the PESTEL framework for identifying the factors, which is a comprehensive framework for strategic planning and decision-making. This study makes use of the TISM methodology for model development and deliberates on the social and environmental implications too, apart from theoretical and managerial implications.

This study aims to investigate the impact of service quality (SQ), perceived value (PV) and consumer satisfaction on Thai street food, with customer satisfaction (CS) as a mediator for customer loyalty and repurchase intention (RI). It also explores how consumer trust (CT) in Thai street food safety moderates these relationships.

Structural equation modelling (SEM) was utilised to analyse the complex interrelationships between various constructs. Multi-group analyses were conducted to investigate the moderating effects of CT on the structural model, considering two distinct groups based on trust levels: low and high.

The findings revealed that SQ and PV significantly influenced CS and behavioural intention, while the perceived quality of Thai street food had no significant impact on post-COVID-19 consumer satisfaction. The study highlighted the critical role of CT in moderating the relationships between SQ, PV and CS, with distinct effects observed in groups with varying trust levels.

The research emphasises the importance of enhancing SQ and delivering value to customers in the context of Thai street food, which can contribute to increased CS, RI and positive word-of-mouth. Furthermore, the study underscores the critical role of building CT in fostering enduring customer relationships and promoting consumer satisfaction and loyalty.

This research offers valuable insights into consumer behaviour and decision-making processes, particularly within the realm of Thai street food. It underscores the significance of understanding and nurturing CT, especially in the post-COVID-19 landscape, emphasising the need for effective business strategies and consumer engagement.